Papers by Kashfia Sailunaz
An exponential growth in the literature in general and the medical literature in particular raise... more An exponential growth in the literature in general and the medical literature in particular raises a need for effective intelligent analysis strategies and tools to provide valuable insights to researchers about the current evolving literature. While existing applications provide more specific approaches to the problem, such as focusing on particular genome or protein information, in this paper, the proposed application provides effective and detailed analysis of PubMed. The developed tool, named KoExPubMed, follows a more generalized and holistic way by taking into consideration different types of information such as authors, countries, genes, and the interactions between them. The developed application consists of four main components; (1) keyword search and ID extraction, (2) PubMed article information and abstract retrieval, (3) country and address extraction, and (4) gene information extraction. In addition to the fundamental components, the tool provides a variety of visualization options for showing the extracted information and the related associations, including line charts for densities and countries, chord charts for collaborations of authors, network graphs for the genes mentioned together, bubble charts for gene frequencies, etc. By addressing the need for a generalized data mining tool, we propose a comprehensive application which is capable of employing data mining and machine learning techniques to extract from PubMed knowledge valuable to researchers and practitioners who are interested in closely investigating the achievements of others.
A survey on brain tumor image analysis
Medical & Biological Engineering & Computing
CNN-Transformer based emotion classification from facial expressions and body gestures
Multimedia Tools and Applications
The aim of this work is to employ face recognition for creating learning profiles of the analysed... more The aim of this work is to employ face recognition for creating learning profiles of the analysed persons who are students in this study. Generating education profiles will help experts in the diagnosis of Attention Deficit Hyperactivity Disorder (ADHD), which is a serious problem in children. Children with ADHD often have the ability and potential to learn. However, it may be difficult to reveal their capabilities and skills. Accordingly, a suffering child may have a hard time succeeding in real life when he/she is ignored and expected to mix with other children. The unrealized gap and deficiency may lead to other problems and more complicated situation with unpredictable consequences. Thanks to the system developed in this study, and the like, which will help in diagnosing the ADHD disease, and hence suffering individuals will be able to recognize their deficiencies, understand their ability to learn and adapt when approached differently in a way which suits his/her situation. This personalized handling of infected students will be an excellent guide to advance their potential and integration within the society carefully and smoothly. The system analyzes the face of a student to inspire his/her emotional state. The reported test results demonstrate how the system works well and produces high accuracy under a variety of severe conditions such as skewed angle, less illumination, accessories etc.
A Comparative Study of Different Pre-Trained Deep Learning Models and Custom CNN for Pancreatic Tumor Detection
The International Arab Journal of Information Technology
Artificial Intelligence and its sub-branches like Machine Learning (ML) and Deep Learning (DL) ap... more Artificial Intelligence and its sub-branches like Machine Learning (ML) and Deep Learning (DL) applications have the potential to have positive effects that can directly affect human life. Medical imaging is briefly making the internal structure of the human body visible with various methods. With deep learning models, cancer detection, which is one of the most lethal diseases in the world, can be made possible with high accuracy. Pancreatic Tumor detection, which is one of the cancer types with the highest fatality rate, is one of the main targets of this project, together with the data set of Computed Tomography images, which is one of the medical imaging techniques and has an effective structure in Pancreatic Cancer imaging. In the field of image classification, which is a computer vision task, the transfer learning technique, which has gained popularity in recent years, has been applied quite frequently. Using pre-trained models were previously trained on a fairly large dataset ...
Brain tumor detection and segmentation: Interactive framework with a visual interface and feedback facility for dynamically improved accuracy and trust
PLOS ONE, 2023
Computers & Electrical Engineering, 2016
Healthcare is one of the basic human rights, but providing proper healthcare in rural remote area... more Healthcare is one of the basic human rights, but providing proper healthcare in rural remote areas of developing countries has always been a challenging task. Telemonitoring has brought some relief with limitations due to accessing patient's health information by healthcare workers and specialists, but merging it with cloud services can be an optimal solution. This proposed framework, CMED (Cloud based MEDical system), is a setup consisting of both healthcare center and a portable healthcare service with a Community Health Worker(CHW), which will sort the patients into three different categories-healthy, alarming and emergency. Upon failing to diagnose a unique problem, it will suggest and contact a specialist doctor and provide suggestions. The privacy and confidentiality of patient's health information will be secured by Identity Based Encryption. In this paper, a framework has been presented for smart and secure healthcare monitoring system for rural under privileged people in developing countries.
PLOS ONE, 2022
Due to the severity and speed of spread of the ongoing Covid-19 pandemic, fast but accurate diagn... more Due to the severity and speed of spread of the ongoing Covid-19 pandemic, fast but accurate diagnosis of Covid-19 patients has become a crucial task. Achievements in this respect might enlighten future efforts for the containment of other possible pandemics. Researchers from various fields have been trying to provide novel ideas for models or systems to identify Covid-19 patients from different medical and non-medical data. AI-based researchers have also been trying to contribute to this area by mostly providing novel approaches of automated systems using convolutional neural network (CNN) and deep neural network (DNN) for Covid-19 detection and diagnosis. Due to the efficiency of deep learning (DL) and transfer learning (TL) models in classification and segmentation tasks, most of the recent AIbased researches proposed various DL and TL models for Covid-19 detection and infected region segmentation from chest medical images like X-rays or CT images. This paper describes a web-based application framework for Covid-19 lung infection detection and segmentation. The proposed framework is characterized by a feedback mechanism for self learning and tuning. It uses variations of three popular DL models, namely Mask R-CNN, U-Net, and U-Net++. The models were trained, evaluated and tested using CT images of Covid patients which were collected from two different sources. The web application provide a simple user friendly interface to process the CT images from various resources using the chosen models, thresholds and other parameters to generate the decisions on detection and segmentation. The models achieve high performance scores for Dice similarity, Jaccard similarity, accuracy, loss, and precision values. The U-Net model outperformed the other models with more than 98% accuracy.
Medical & Biological Engineering & Computing
The ongoing COVID-19 pandemic caused by the SARS-CoV-2 virus has already resulted in 6.6 million ... more The ongoing COVID-19 pandemic caused by the SARS-CoV-2 virus has already resulted in 6.6 million deaths with more than 637 million people infected after only 30 months since the first occurrences of the disease in December 2019. Hence, rapid and accurate detection and diagnosis of the disease is the first priority all over the world. Researchers have been working on various methods for COVID-19 detection and as the disease infects lungs, lung image analysis has become a popular research area for detecting the presence of the disease. Medical images from chest X-rays (CXR), computed tomography (CT) images, and lung ultrasound images have been used by automated image analysis systems in artificial intelligence (AI)-and machine learning (ML)-based approaches. Various existing and novel ML, deep learning (DL), transfer learning (TL), and hybrid models have been applied for detecting and classifying COVID-19, segmentation of infected regions, assessing the severity, and tracking patient progress from medical images of COVID-19 patients. In this paper, a comprehensive review of some recent approaches on COVID-19-based image analyses is provided surveying the contributions of existing research efforts, the available image datasets, and the performance metrics used in recent works. The challenges and future research scopes to address the progress of the fight against COVID-19 from the AI perspective are also discussed. The main objective of this paper is therefore to provide a summary of the research works done in COVID detection and analysis from medical image datasets using ML, DL, and TL models by analyzing their novelty and efficiency while mentioning other COVID-19-based review/survey researches to deliver a brief overview on the maximum amount of information on COVID-19-based existing researches. Keywords COVID-19 • Medical image analysis • Machine learning • Deep learning • Transfer learning • Computer tomography Abbreviations AI Artificial intelligence ANN Artificial neural network ARDS Acute respiratory distress dyndrome AUC Area under the curve
Convex Hull in Brain Tumor Segmentation
Lecture Notes in Computer Science, 2022
Tweet and user validation with supervised feature ranking and rumor classification
Multimedia Tools and Applications
A Multi-Modal Emotion Recognition System Based on CNN-Transformer Deep Learning Technique
2022 7th International Conference on Data Science and Machine Learning Applications (CDMA), 2022
Emotion analysis is a subject that researchers from various fields have been working on for a lon... more Emotion analysis is a subject that researchers from various fields have been working on for a long time. Different emotion detection methods have been developed for text, audio, photography, and video domains. Automated emotion detection methods using machine learning and deep learning models from videos and pictures have been an interesting topic for researchers. In this paper, a deep learning framework, in which CNN and Transformer models are combined, that classifies emotions using facial and body features extracted from videos is proposed. Facial and body features were extracted using OpenPose, and in the data preprocessing stage 2 operations such as new video creation and frame selection were tried. The experiments were conducted on two datasets, FABO and CK+. Our framework outperformed similar deep learning models with 99% classification accuracy for the FABO dataset, and showed remarkable performance over 90% accuracy for most versions of the framework for both the FABO and CK+ dataset.
Temporal Dependency Between Evolution of Features and Dynamic Social Networks
Applications of Data Management and Analysis, 2018
The complexity of analyzing dynamic social network data is higher than static networks because of... more The complexity of analyzing dynamic social network data is higher than static networks because of the nature of the data itself. Nodes and links of dynamic social networks are unlabeled, not identically distributed and the effect of different features of the network evolves over time with the evolution of the network links. Changes in features of nodes initiate the changes in links and groups of the network and vice versa. The correlation between these changes is not parallel which makes things more complicated. The temporal effect increases the complexity of the evaluation by adding another vital parameter to the problem. In this paper, the effect of the temporal dependency on dynamic social network evolution was examined using a real life social media dataset. By extracting the most prominent features of the network after a specific time period with an unsupervised feature selection method, we computed the correlation between their evolution which was not uniformly distributed over the time span.
Pancreatic Tumor Detection by Convolutional Neural Networks
2022 International Arab Conference on Information Technology (ACIT)
Text-Based Analysis of Emotion by Considering Tweets
Machine Learning Techniques for Online Social Networks
People express their emotions in various ways, including facial expression, gesture, speech, spee... more People express their emotions in various ways, including facial expression, gesture, speech, speech frequency, writing, etc. In today’s world where almost every person interacts with other people via social networking and social media, the emotional state of a person can be determined by analyzing the text collected from his/her posts and comments. Although emotion extraction and analysis from text posted in social networks and social media like facebook, twitter, etc. is a very challenging task, still it can give researchers a valuable insight into the complexity of human emotions. In this paper, test from tweets has been used for detecting 32 primary human emotions and then the emotions were analyzed against gender, location, and temporal information of the considered people.
J. Comput. Sci., 2019
Online social networks have emerged as new platform that provide an arena for people to share the... more Online social networks have emerged as new platform that provide an arena for people to share their views and perspectives on different issues and subjects with their friends, family, relatives, etc. We can share our thoughts, mental state, moments, stand on specific social, national, international issues through text, photos, audio and video messages and posts. Indeed, despite the availability of other forms of communication, text is still one of the most common ways of communication in a social network. The target of the work described in this paper is to detect and analyze sentiment and emotion expressed by people from text in their twitter posts and use them for generating recommendations. We collected tweets and replies on few specific topics and created a dataset with text, user, emotion, sentiment information, etc. We used the dataset to detect sentiment and emotion from tweets and their replies and measured the influence scores of users based on various user-based and tweet-...
Global journal of computer science and technology, 2015
Search engines rank web pages according to different conditions. Some of them use publication tim... more Search engines rank web pages according to different conditions. Some of them use publication time, some use last time of update, some checks the currency of the content of the web page. In this paper, a new algorithm is proposed which will work on the time of the web page, temporal information of the content and forms a binary tree to rank among web pages.
Learning By Creating Instructional Videos: An Experience Report from a Database Course
2020 IEEE Global Engineering Education Conference (EDUCON), 2020
We report on an experiment in the final two weeks of a third-year course on Database Systems wher... more We report on an experiment in the final two weeks of a third-year course on Database Systems where we asked the students to create instructional videos to teach their peers a self-chosen concept that they found challenging to learn. After the videos were submitted, we surveyed the students to see why they chose the video subjects, whether creating the video helped them understand the chosen subject deeper, where they focused their time when creating the video, whether they enjoyed the assignment, and whether they prefer a more conventional assignment. It was concerning that only 78% of the registered students submitted the assignment. About 52% of the students who completed the assignment took an online survey. An overwhelming majority of the surveyed students indicated that the assignment helped them understand the topic they chose in a more subtle way. However, the class was split on whether they prefer this style of active learning assignments over the more conventional type.
Temporal Dependency Between Evolution of Features and Dynamic Social Networks
The complexity of analyzing dynamic social network data is higher than static networks because of... more The complexity of analyzing dynamic social network data is higher than static networks because of the nature of the data itself. Nodes and links of dynamic social networks are unlabeled, not identically distributed and the effect of different features of the network evolves over time with the evolution of the network links. Changes in features of nodes initiate the changes in links and groups of the network and vice versa. The correlation between these changes is not parallel which makes things more complicated. The temporal effect increases the complexity of the evaluation by adding another vital parameter to the problem. In this paper, the effect of the temporal dependency on dynamic social network evolution was examined using a real life social media dataset. By extracting the most prominent features of the network after a specific time period with an unsupervised feature selection method, we computed the correlation between their evolution which was not uniformly distributed ove...
CMED: Cloud based medical system framework for rural health monitoring in developing countries
Computers & Electrical Engineering, 2016
Abstract Healthcare is one of the basic human rights, but providing proper healthcare in rural re... more Abstract Healthcare is one of the basic human rights, but providing proper healthcare in rural remote areas of developing countries has always been a challenging task. Telemonitoring has brought some relief with limitations due to accessing patient's health information by healthcare workers and specialists, but merging it with cloud services can be an optimal solution. This proposed framework, CMED (Cloud based MEDical system), is a setup consisting of both healthcare center and a portable healthcare service with a Community Health Worker(CHW), which will sort the patients into three different categories—healthy, alarming and emergency. Upon failing to diagnose a unique problem, it will suggest and contact a specialist doctor and provide suggestions. The privacy and confidentiality of patient's health information will be secured by Identity Based Encryption. In this paper, a framework has been presented for smart and secure healthcare monitoring system for rural under privileged people in developing countries.
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Papers by Kashfia Sailunaz